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Urban Heat All Areas Portland Tacoma King County iNaturalist City Inventories

CITY INVENTORIES

library(tidyverse)
library(knitr)
library(kableExtra)
library(gghalves)
library(patchwork)
library(scales)
library(ggmap)
library(osmdata)

Purpose

Data Wrangling

Import Data

portland.parks <- read.csv('https://raw.githubusercontent.com/jmhulbert/redhot/main/data/Portland.Parks.Trees.AF.csv')
portland.streets <- read.csv('https://raw.githubusercontent.com/jmhulbert/redhot/main/data/Portland.Street.Trees.AF.csv')
seattle.combined <- read.csv('https://raw.githubusercontent.com/jmhulbert/redhot/main/data/Seattle.Trees.AF.csv')

Tidy Data

portland.parks <- portland.parks[c(3,4,6,7,8,14,35,36,40,42:45)]
portland.parks <- portland.parks %>% mutate(city.space="Park")

portland.streets <- portland.streets[c(3,4,5,9,10:19)]
portland.streets <- portland.streets %>% mutate(city.space="Street") %>% rename(DBH=DIAMETER) %>% rename(Functional=FUNCTIONAL)

portland.combined <- full_join(portland.streets,portland.parks,by = join_by(OBJECTID, Condition, DN_AF1, lat, lon, DBH,city.space,Functional)) %>% mutate(city="Portland") %>% mutate(DN_AFc=DN_AF1)

portland.combined$DN_AF1 <- ((portland.combined$DN_AF1 * 1.8) +32)
seattle.combined <- seattle.combined[c(1:8,11:14,17:19)] %>% mutate(city="Seattle") %>% rename(GlobalID=GLOBALID)

seattle.streets <- seattle.combined %>% filter(Source.Department=="Seattle Department of Transportation") %>% mutate(city.space="Street")
seattle.parks <- seattle.combined %>% filter(Source.Department=="Seattle Parks and Recreation") %>% mutate(city.space="Park")

seattle.combined.back <- rbind(seattle.streets,seattle.parks)

Categorize Trees

  • Portland Top-Dieback Probability Categories (10%)
    • Most Concern (>10%): > 88.5
    • Moderate Concern (2.5-10%): 84.32-88.5
    • Lowest Concern (<2.5%): <84.32
  • Portland Dieback Probability Cateogories (25%)
    • Most Concern (>25%): >91.66
    • Moderate Concern (5-25%): 86.48- 91.66
    • Lowest Concern (<5%): <86.48
portland.most <- portland.combined %>% filter(DN_AF1>88.5)%>% mutate(Category1="Most Concern") %>% droplevels()
portland.mod <- portland.combined %>% filter(DN_AF1>84.32 & DN_AF1<88.5) %>% mutate(Category1="Moderate Concern") %>% droplevels()
portland.low <- portland.combined %>% filter(DN_AF1<84.32) %>% mutate(Category1="Lowest Concern") %>% droplevels()

portland.combined.cat <- bind_rows(list(portland.low,portland.mod,portland.most))

portland.most <- portland.combined.cat %>% filter(DN_AF1>86.5)%>% mutate(Category1Plus2="Most Concern") %>% droplevels()
portland.mod <- portland.combined.cat %>% filter(DN_AF1>82.32 & DN_AF1<86.5) %>% mutate(Category1Plus2="Moderate Concern") %>% droplevels()
portland.low <- portland.combined.cat %>% filter(DN_AF1<82.32) %>% mutate(Category1Plus2="Lowest Concern") %>% droplevels()

portland.combined.cat <- bind_rows(list(portland.low,portland.mod,portland.most))

portland.most <- portland.combined.cat %>% filter(DN_AF1>91.66)%>% mutate(Category25="Most Concern") %>% droplevels()
portland.mod <- portland.combined.cat %>% filter(DN_AF1>86.48 & DN_AF1<91.66) %>% mutate(Category25="Moderate Concern") %>% droplevels()
portland.low <- portland.combined.cat %>% filter(DN_AF1<86.48) %>% mutate(Category25="Lowest Concern") %>% droplevels()


portland.combined.cat <- bind_rows(list(portland.low,portland.mod,portland.most))
#P1 <- ggplot(data.top, aes(x=DN_AF1))+geom_histogram() + theme_bw()
  • King County Top-Dieback Probability Categories (10%)
    • Most Concern (>10%): >87.8
    • Moderate Concern (2.5-10%): 85.22-87.8
    • Lowest Concern (<2.5%): <85.22
  • King County Top-Dieback Probability Categories (25%)
    • Most Concern (>25%): 89.78
    • Moderate Concern (5-25%): 86.5 - 89.78
    • Lowest Concern (<5%): <86.5
seattle.most <- seattle.combined.back %>% filter(DN_AF1>87.8)%>% mutate(Category1="Most Concern") %>% droplevels()
seattle.mod <- seattle.combined.back %>% filter(DN_AF1>85.22 & DN_AF1<87.8) %>% mutate(Category1="Moderate Concern") %>% droplevels()
seattle.low <- seattle.combined.back %>% filter(DN_AF1<85.22) %>% mutate(Category1="Lowest Concern") %>% droplevels()

seattle.combined.cat <- bind_rows(list(seattle.low,seattle.mod,seattle.most))

seattle.most <- seattle.combined.cat %>% filter(DN_AF1>85.8)%>% mutate(Category1Plus2="Most Concern") %>% droplevels()
seattle.mod <- seattle.combined.cat%>% filter(DN_AF1>83.22 & DN_AF1<85.8) %>% mutate(Category1Plus2="Moderate Concern") %>% droplevels()
seattle.low <- seattle.combined.cat %>% filter(DN_AF1<83.22) %>% mutate(Category1Plus2="Lowest Concern") %>% droplevels()

seattle.combined.cat <- bind_rows(list(seattle.low,seattle.mod,seattle.most))

seattle.most <- seattle.combined.cat %>% filter(DN_AF1>89.78)%>% mutate(Category25="Most Concern") %>% droplevels()
seattle.mod <- seattle.combined.cat %>% filter(DN_AF1>86.5 & DN_AF1<89.78) %>% mutate(Category25="Moderate Concern") %>% droplevels()
seattle.low <- seattle.combined.cat%>% filter(DN_AF1<86.5) %>% mutate(Category25="Lowest Concern") %>% droplevels()

seattle.combined.cat <- bind_rows(list(seattle.low,seattle.mod,seattle.most))

87 trees in Seattle did not have UHI data. 1 tree in Portland did not have UHI data.

Join City Data

cities <- full_join(seattle.combined.cat,portland.combined.cat,by = join_by(OBJECTID, DBH, DN_AF1, lat, lon, city, city.space,GlobalID,Category1,Category1Plus2,Category25))

Summarize Data

summary <- cities %>% group_by(city,city.space,Category1) %>% summarize(n=n(),meanAF=mean(DN_AF1,na.rm=TRUE),n_AF_na=sum(is.na(DN_AF1)))
## `summarise()` has grouped output by 'city', 'city.space'. You can override
## using the `.groups` argument.
summary
## # A tibble: 12 × 6
## # Groups:   city, city.space [4]
##    city     city.space Category1            n meanAF n_AF_na
##    <chr>    <chr>      <chr>            <int>  <dbl>   <int>
##  1 Portland Park       Lowest Concern      89   83.3       0
##  2 Portland Park       Moderate Concern   266   87.6       0
##  3 Portland Park       Most Concern       609   89.1       0
##  4 Portland Street     Lowest Concern      84   83.2       0
##  5 Portland Street     Moderate Concern   763   87.5       0
##  6 Portland Street     Most Concern       867   89.0       0
##  7 Seattle  Park       Lowest Concern       5   83.8       0
##  8 Seattle  Park       Moderate Concern   434   87.1       0
##  9 Seattle  Park       Most Concern       371   88.5       0
## 10 Seattle  Street     Lowest Concern      44   84.3       0
## 11 Seattle  Street     Moderate Concern  1205   87.0       0
## 12 Seattle  Street     Most Concern       544   88.4       0
ggplot(summary,aes(n,city.space,fill=Category1))+geom_col(position=position_dodge(),alpha=0.7) +facet_wrap(~city) +theme_bw() + scale_fill_manual(name="Category",values=c("#7fcdbb","#fe9929","#DDA0DD")) +labs(x="Number of Trees",y="Tree Inventory")

summary <- cities %>% group_by(city,Category1) %>% summarize(n=n(),meanAF=mean(DN_AF1,na.rm=TRUE),n_AF_na=sum(is.na(DN_AF1)))
## `summarise()` has grouped output by 'city'. You can override using the
## `.groups` argument.
summary
## # A tibble: 6 × 5
## # Groups:   city [2]
##   city     Category1            n meanAF n_AF_na
##   <chr>    <chr>            <int>  <dbl>   <int>
## 1 Portland Lowest Concern     173   83.2       0
## 2 Portland Moderate Concern  1029   87.5       0
## 3 Portland Most Concern      1476   89.1       0
## 4 Seattle  Lowest Concern      49   84.2       0
## 5 Seattle  Moderate Concern  1639   87.0       0
## 6 Seattle  Most Concern       915   88.4       0

Portland

summary1 <- portland.combined.cat %>% group_by(city.space,Category1) %>% summarize(n=n(),meanAF=mean(DN_AF1,na.rm=TRUE),n_AF_na=sum(is.na(DN_AF1)))
## `summarise()` has grouped output by 'city.space'. You can override using the
## `.groups` argument.
summary1
## # A tibble: 6 × 5
## # Groups:   city.space [2]
##   city.space Category1            n meanAF n_AF_na
##   <chr>      <chr>            <int>  <dbl>   <int>
## 1 Park       Lowest Concern      89   83.3       0
## 2 Park       Moderate Concern   266   87.6       0
## 3 Park       Most Concern       609   89.1       0
## 4 Street     Lowest Concern      84   83.2       0
## 5 Street     Moderate Concern   763   87.5       0
## 6 Street     Most Concern       867   89.0       0
summary1Plus2 <- portland.combined.cat %>% group_by(city.space,Category1Plus2) %>% summarize(n=n(),meanAF=mean(DN_AF1,na.rm=TRUE),n_AF_na=sum(is.na(DN_AF1)))
## `summarise()` has grouped output by 'city.space'. You can override using the
## `.groups` argument.
summary1
## # A tibble: 6 × 5
## # Groups:   city.space [2]
##   city.space Category1            n meanAF n_AF_na
##   <chr>      <chr>            <int>  <dbl>   <int>
## 1 Park       Lowest Concern      89   83.3       0
## 2 Park       Moderate Concern   266   87.6       0
## 3 Park       Most Concern       609   89.1       0
## 4 Street     Lowest Concern      84   83.2       0
## 5 Street     Moderate Concern   763   87.5       0
## 6 Street     Most Concern       867   89.0       0
summary25 <- portland.combined.cat %>% group_by(city.space,Category25) %>% summarize(n=n(),meanAF=mean(DN_AF1,na.rm=TRUE),n_AF_na=sum(is.na(DN_AF1)))
## `summarise()` has grouped output by 'city.space'. You can override using the
## `.groups` argument.
summary25
## # A tibble: 4 × 5
## # Groups:   city.space [2]
##   city.space Category25           n meanAF n_AF_na
##   <chr>      <chr>            <int>  <dbl>   <int>
## 1 Park       Lowest Concern     134   84.1       0
## 2 Park       Moderate Concern   830   88.8       0
## 3 Street     Lowest Concern     232   84.7       0
## 4 Street     Moderate Concern  1482   88.6       0
ggplot(summary1,aes(n,city.space,fill=Category1))+geom_col(position=position_dodge(),alpha=0.7) +theme_bw() + scale_fill_manual(name="Category 1",values=c("#7fcdbb","#fe9929","#DDA0DD")) +labs(x="Number of Trees",y="Tree Inventory")

ggplot(summary1Plus2,aes(n,city.space,fill=Category1Plus2))+geom_col(position=position_dodge(),alpha=0.7) +theme_bw() + scale_fill_manual(name="Category 1 plus 2",values=c("#7fcdbb","#fe9929","#DDA0DD")) +labs(x="Number of Trees",y="Tree Inventory")

ggplot(summary25,aes(n,city.space,fill=Category25))+geom_col(position=position_dodge(),alpha=0.7) +theme_bw() + scale_fill_manual(name="Category 25",values=c("#7fcdbb","#fe9929","#DDA0DD")) +labs(x="Number of Trees",y="Tree Inventory")

Portland Map

portbb <- c(left = min(portland.combined.cat$lon), 
                              bottom = min(portland.combined.cat$lat), 
                              right = max(portland.combined.cat$lon), 
                              top = max(portland.combined.cat$lat))
portmap <- get_map(portbb, zoom = 11, scale = 2, maptype="terrain",source="google")
## ! Bounding box given to Google - spatial extent only approximate.
## ℹ <https://maps.googleapis.com/maps/api/staticmap?center=45.543068,-122.632381&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx>
portland.map <- ggmap(portmap) + geom_point(data = portland.combined.cat, aes(x = lon, y = lat,fill=DN_AF1), color = "black",pch=21, size = 3) + theme_minimal() +scale_fill_viridis_c(option = "inferno")+labs(title="Portland",x="Longitude",y="Latitude",fill="Afternoon\nTemp (F)") +theme(plot.title = element_text(size = 14, hjust = .5,face = "bold.italic"))
portland.map

portland.map.cat1 <- ggmap(portmap) + geom_point(data = portland.combined.cat, aes(x = lon, y = lat,fill=Category1), color = "black",pch=21, size = 3) + theme_minimal() +scale_fill_manual(name="Category",values=c("#DDA0DD","#7fcdbb","#fe9929"))+labs(title="Portland",x="Longitude",y="Latitude",fill="Conservation\nCategory") +theme(plot.title = element_text(size = 14, hjust = .5,face = "bold.italic"))
portland.map.cat1Plus2 <- ggmap(portmap) + geom_point(data = portland.combined.cat, aes(x = lon, y = lat,fill=Category1Plus2), color = "black",pch=21, size = 3) + theme_minimal() +scale_fill_manual(name="Category",values=c("#DDA0DD","#7fcdbb","#fe9929"))+labs(title="Portland",x="Longitude",y="Latitude",fill="Conservation\nCategory") +theme(plot.title = element_text(size = 14, hjust = .5,face = "bold.italic"))
portland.map.cat25 <- ggmap(portmap) + geom_point(data = portland.combined.cat, aes(x = lon, y = lat,fill=Category25), color = "black",pch=21, size = 3) + theme_minimal() +scale_fill_manual(name="Category",values=c("#DDA0DD","#7fcdbb","#fe9929"))+labs(title="Portland",x="Longitude",y="Latitude",fill="Conservation\nCategory") +theme(plot.title = element_text(size = 14, hjust = .5,face = "bold.italic"))
portland.map.cat1 <- portland.map.cat1  +labs(title="0.1 Probability") + theme(legend.position = "none")
portland.map.cat1Plus2 <- portland.map.cat1Plus2  +labs(title="0.1 Probability at 2 degrees higher") + theme(legend.position = "none",axis.title.y = element_blank())
portland.map.cat25 <-  portland.map.cat25  +labs(title="0.25 Probability") + theme(axis.title.y = element_blank())
portland.map.cat1 + portland.map.cat1Plus2 + portland.map.cat25

Seattle

summary1 <- seattle.combined.cat %>% group_by(city.space,Category1) %>% summarize(n=n(),meanAF=mean(DN_AF1,na.rm=TRUE),n_AF_na=sum(is.na(DN_AF1)))
## `summarise()` has grouped output by 'city.space'. You can override using the
## `.groups` argument.
summary1
## # A tibble: 6 × 5
## # Groups:   city.space [2]
##   city.space Category1            n meanAF n_AF_na
##   <chr>      <chr>            <int>  <dbl>   <int>
## 1 Park       Lowest Concern       5   83.8       0
## 2 Park       Moderate Concern   434   87.1       0
## 3 Park       Most Concern       371   88.5       0
## 4 Street     Lowest Concern      44   84.3       0
## 5 Street     Moderate Concern  1205   87.0       0
## 6 Street     Most Concern       544   88.4       0
summary1Plus2 <- seattle.combined.cat %>% group_by(city.space,Category1Plus2) %>% summarize(n=n(),meanAF=mean(DN_AF1,na.rm=TRUE),n_AF_na=sum(is.na(DN_AF1)))
## `summarise()` has grouped output by 'city.space'. You can override using the
## `.groups` argument.
summary1
## # A tibble: 6 × 5
## # Groups:   city.space [2]
##   city.space Category1            n meanAF n_AF_na
##   <chr>      <chr>            <int>  <dbl>   <int>
## 1 Park       Lowest Concern       5   83.8       0
## 2 Park       Moderate Concern   434   87.1       0
## 3 Park       Most Concern       371   88.5       0
## 4 Street     Lowest Concern      44   84.3       0
## 5 Street     Moderate Concern  1205   87.0       0
## 6 Street     Most Concern       544   88.4       0
summary25 <- seattle.combined.cat %>% group_by(city.space,Category25) %>% summarize(n=n(),meanAF=mean(DN_AF1,na.rm=TRUE),n_AF_na=sum(is.na(DN_AF1)))
## `summarise()` has grouped output by 'city.space'. You can override using the
## `.groups` argument.
summary25
## # A tibble: 6 × 5
## # Groups:   city.space [2]
##   city.space Category25           n meanAF n_AF_na
##   <chr>      <chr>            <int>  <dbl>   <int>
## 1 Park       Lowest Concern      90   86.0       0
## 2 Park       Moderate Concern   706   87.9       0
## 3 Park       Most Concern        14   90.2       0
## 4 Street     Lowest Concern     302   85.8       0
## 5 Street     Moderate Concern  1474   87.6       0
## 6 Street     Most Concern        17   90.4       0
ggplot(summary1,aes(n,city.space,fill=Category1))+geom_col(position=position_dodge(),alpha=0.7) +theme_bw() + scale_fill_manual(name="Category",values=c("#DDA0DD","#7fcdbb","#fe9929")) +labs(x="Number of Trees",y="Tree Inventory")

ggplot(summary1Plus2,aes(n,city.space,fill=Category1Plus2))+geom_col(position=position_dodge(),alpha=0.7) +theme_bw() + scale_fill_manual(name="Category",values=c("#DDA0DD","#7fcdbb","#fe9929")) +labs(x="Number of Trees",y="Tree Inventory")

ggplot(summary25,aes(n,city.space,fill=Category25))+geom_col(position=position_dodge(),alpha=0.7) +theme_bw() + scale_fill_manual(name="Category",values=c("#DDA0DD","#7fcdbb","#fe9929")) +labs(x="Number of Trees",y="Tree Inventory")

Seattle Map

seabb <- c(left = min(seattle.combined.cat$lon), 
                              bottom = min(seattle.combined.cat$lat), 
                              right = max(seattle.combined.cat$lon), 
                              top = max(seattle.combined.cat$lat))
seamap <- get_map(seabb, zoom = 11, scale = 2, maptype="terrain",source="google")
## ! Bounding box given to Google - spatial extent only approximate.
## ℹ <https://maps.googleapis.com/maps/api/staticmap?center=47.616324,-122.334218&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx>
seattle.map <- ggmap(seamap) + geom_point(data = seattle.combined.cat, aes(x = lon, y = lat,fill=DN_AF1), color = "black",pch=21, size = 3) + theme_minimal() +scale_fill_viridis_c(option = "inferno")+labs(title="Seattle",x="Longitude",y="Latitude",fill="Afternoon\nTemp (F)") +theme(plot.title = element_text(size = 14, hjust = .5,face = "bold.italic"))
seattle.map

seattle.map.cat1 <- ggmap(seamap) + geom_point(data = seattle.combined.cat, aes(x = lon, y = lat,fill=Category1), color = "black",pch=21, size = 3) + theme_minimal() +scale_fill_manual(name="Category",values=c("#DDA0DD","#7fcdbb","#fe9929"))+labs(title="Seattle",x="Longitude",y="Latitude",fill="Conservation\nCategory") +theme(plot.title = element_text(size = 14, hjust = .5,face = "bold.italic"))
seattle.map.cat1Plus2 <- ggmap(seamap) + geom_point(data = seattle.combined.cat, aes(x = lon, y = lat,fill=Category1Plus2), color = "black",pch=21, size = 3) + theme_minimal() +scale_fill_manual(name="Category",values=c("#DDA0DD","#7fcdbb","#fe9929"))+labs(title="Seattle",x="Longitude",y="Latitude",fill="Conservation\nCategory") +theme(plot.title = element_text(size = 14, hjust = .5,face = "bold.italic"))
seattle.map.cat25 <- ggmap(seamap) + geom_point(data = seattle.combined.cat, aes(x = lon, y = lat,fill=Category25), color = "black",pch=21, size = 3) + theme_minimal() +scale_fill_manual(name="Category",values=c("#DDA0DD","#7fcdbb","#fe9929"))+labs(title="Seattle",x="Longitude",y="Latitude",fill="Conservation\nCategory") +theme(plot.title = element_text(size = 14, hjust = .5,face = "bold.italic"))
seattle.map.cat1 <- seattle.map.cat1  +labs(title="0.1 Probability") + theme(legend.position = "none")
seattle.map.cat1Plus2 <- seattle.map.cat1Plus2  +labs(title="0.1 Probability at 2 degrees higher") + theme(legend.position = "none",axis.title.y = element_blank())
seattle.map.cat25 <-  seattle.map.cat25  +labs(title="0.25 Probability") + theme(axis.title.y = element_blank())
seattle.map.cat1 + seattle.map.cat1Plus2 + seattle.map.cat25

portland.map.cat1 <- ggmap(portmap) + geom_point(data = portland.combined.cat, aes(x = lon, y = lat,fill=Category1), color = "black",pch=21, size = 3) + theme_minimal() +scale_fill_manual(name="Category",values=c("#DDA0DD","#7fcdbb","#fe9929"))+labs(title="Portland",x="Longitude",y="Latitude",fill="Conservation\nCategory") +theme(plot.title = element_text(size = 14, hjust = .5,face = "bold.italic"))
seattle.map <- seattle.map + theme(legend.position = "none",axis.title.x = element_blank())
seattle.map.cat  <- seattle.map.cat1 + theme(legend.position = "none",plot.title = element_blank())
portland.map <- portland.map +theme(axis.title.x = element_blank(),axis.title.y = element_blank())
portland.map.cat <- portland.map.cat1 +theme(plot.title = element_blank(),axis.title.y = element_blank())


#+ theme(legend.position = "none",,)
(seattle.map | portland.map) / (seattle.map.cat | portland.map.cat)